Media applications which include video and audio database management, database browsing and identification are undergoing explosive growth and are expected to continue to grow. To address this growth, there is a need for a comprehensive solution related to the problem of creating a video sequence database and identifying, within such a database, a particular video sequence or sequences that are tolerant of media content distortions. Multiple applications include video database mining, copyright content detection for video hosting web-sites, contextual advertising placement, and broadcast monitoring of video programming and advertisements.
Multimedia fingerprinting refers to the ability to generate associated identifying data, referred to as a fingerprint, from the multimedia image, audio and video content. A fingerprint ideally has several properties. First, the fingerprint should be much smaller than the original data. Second, the fingerprint should be designed such that it can be searched for in a large database of fingerprints. Third, the original multimedia content should not be able to be reconstructed from the fingerprint. Fourth, for multimedia content that is a distorted version of another multimedia content, fingerprints of the original and distorted versions should be similar. Examples of some common multimedia distortions include, selecting a clip of video content temporally, cropping the image data, re-encoding the image or audio data to a lower bit-rate, changing a frame rate of the video or audio content, re-recording the multimedia data via some analog medium such as a camcorder in a movie theatre, and changing the aspect ratio of the image content. A fingerprint with the fourth property is deemed to be robust against such distortions. Such a system of fingerprinting and search is preferable to other methods of content identification. For example, multimedia watermarking changes the multimedia content by inserting watermark data. Unlike multimedia watermarking, fingerprinting does not change the content. Fingerprinting is, however, a very challenging problem.
Increasing demand for such fingerprinting and search solutions, which include standard definition (SD) and high definition (HD) formats of video, requires increasing sophistication, flexibility, and performance in the supporting algorithms and hardware. The sophistication, flexibility, and performance that are desired exceed the capabilities of current generations of software based solutions, in many cases, by an order of magnitude.